j_fun#
- sionna.phy.fec.utils.j_fun(mu: torch.Tensor) torch.Tensor[source]#
Computes the J-function.
The J-function relates mutual information to the mean of Gaussian-distributed Log-Likelihood Ratios (LLRs) using the Gaussian approximation. This function implements the approximation proposed in [Brannstrom]:
\[J(\mu) \approx \left( 1 - 2^{H_\text{1}(2\mu)^{H_\text{2}}}\right)^{H_\text{3}}\]where \(\mu\) represents the mean of the LLR distribution, and the constants are defined as \(H_\text{1}=0.3073\), \(H_\text{2}=0.8935\), and \(H_\text{3}=1.1064\).
Input values are clipped to [1e-10, 1000] for numerical stability.
- Parameters:
mu (torch.Tensor) – Tensor of arbitrary shape, representing the mean of the LLR values.
- Outputs:
mi – Tensor of the same shape and dtype as
mu, containing the calculated mutual information values.
Examples
import torch from sionna.phy.fec.utils import j_fun mu = torch.tensor([0.1, 1.0, 5.0]) mi = j_fun(mu) print(mi)